Journal of Korean Society for Atmospheric Environment, v.41, no.3, pp.403 - 429
Abstract
The rapid economic and industrial development in East Asia has led to a significant increase in precursor gas emissions, exacerbating secondary air pollution and contributing to regional air quality deterioration. In particular, the formation and transport of secondary pollutants such as ozone (O3) and fine particulate matter (PM2.5) have become major environmental concerns. This study first outlines major modeling approaches for air pollutants-including chemical transport models (CTMs) and recent hybrid frameworks integrating machine learning-and then examines key findings from studies conducted between 2020 and 2024, with a focus on precursor emissions, chemical transformation processes, and the role of meteorological conditions in pollutant formation and transport. In addition, we synthesize recent evidence highlighting the increasing relevance of climate-air quality interactions and the need for cross-national collaboration to improve modeling consistency. Based on this review, we propose future directions for air quality modeling, emphasizing enhanced computational efficiency, more accurate representation of atmospheric processes, and expanded applicability to policy support. Based on this review, we propose recommendations for next-generation air quality models, emphasizing enhanced computational efficiency, improved representation of atmospheric processes, and greater applicability for policy support in the region